02_visualization
anjie & gal
2/16/2021
library(here)
library(tidyverse)
library(stringr) # for parsing r string
library(jsonlite) # for parsing r string
library(ggiraphExtra)
library(plotrix)RT_data <- read_csv(here('data/processed_data/trimmed_RTdata.csv'))
pref_data <- read_csv(here('data/processed_data/trimmed_prefdata.csv'))
similarity_data <- read_csv(here('data/processed_data/trimmed_similaritydata.csv'))
complexity_data <- read_csv(here('data/processed_data/trimmed_complexitydata.csv'))
demog_data <- read_csv(here('data/processed_data/trimmed_demogdata.csv'))Descriptive Info
N = 167
sample size
RT_data %>%
distinct(subject) %>%
count()## # A tibble: 1 x 1
## n
## <int>
## 1 161demographic
age
demog_data %>%
ggplot(aes(x = age)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ethnicity
demog_data %>%
ggplot(aes(x = ethnicity)) +
geom_histogram(stat = "count") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))## Warning: Ignoring unknown parameters: binwidth, bins, pad
Gender
demog_data %>%
ggplot(aes(x = gender)) +
geom_histogram(stat = "count") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))## Warning: Ignoring unknown parameters: binwidth, bins, pad
education
demog_data %>%
ggplot(aes(x = education)) +
geom_histogram(stat = "count") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))## Warning: Ignoring unknown parameters: binwidth, bins, pad
RT raw
overall
RT_data %>%
ggplot(aes(x = rt)) +
geom_histogram() +
scale_x_log10() ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
by trial type
RT_data %>%
ggplot(aes(x = rt)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~trial_type)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
by trial complexity
RT_data %>%
ggplot(aes(x = rt)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~trial_complexity)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
by block type
RT_data %>%
ggplot(aes(x = rt)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~block_type)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
by item id
simple
RT_data %>%
filter(trial_complexity == "simple") %>%
ggplot(aes(x = rt)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~item_id)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
complex
RT_data %>%
filter(trial_complexity == "complex") %>%
ggplot(aes(x = rt)) +
geom_histogram() +
scale_x_log10() +
facet_wrap(~item_id)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
RT basic plotting
normal
summarized <- RT_data %>%
group_by(trial_number, item_type, trial_complexity) %>%
summarise(meanRT=mean(rt), maxRT=max(rt), minRT=min(rt), medianRT=median(rt), Std=sd(rt), SE = std.error(rt), n = n())## `summarise()` regrouping output by 'trial_number', 'item_type' (override with `.groups` argument)ggplot(summarized, aes(x=trial_number, y=medianRT, colour=item_type)) + geom_line() + geom_errorbar(aes(ymin=medianRT-SE, ymax=medianRT+SE),
width=0.8, size = 0.8, position = position_dodge(width = 0.2), show.legend = FALSE, alpha = 0.8) +
geom_point(position = position_dodge(width = 0.2), size=2.5) + geom_line(size=1.2, position = position_dodge(width = 0.2)) + ylab("RT [msec]") +
theme_gray() + theme(
panel.grid.minor = element_blank(),
plot.title = element_text(hjust=0.5, size=22, face="bold"),
axis.title.x = element_text(size=18, face='bold'),
axis.title.y = element_text(size=18, face='bold'),
axis.text = element_text(size=15),
legend.title = element_blank(),
legend.text = element_text(size=14, face='bold'),
strip.text = element_text(size=17, face='bold'),
legend.key.size = unit(2.5, 'lines')) +
scale_x_continuous(name="Trial Number", breaks =c(2,4,6,8)) +
facet_grid(~trial_complexity)
log transformed
summarized_log <- RT_data %>%
group_by(trial_number, item_type, trial_complexity) %>%
summarise(meanRT=mean(log(rt)),
maxRT=max(log(rt)),
minRT=min(log(rt)),
medianRT=median(log(rt)),
Std=sd(log(rt)),
SE = std.error(log(rt)),
n = n())## `summarise()` regrouping output by 'trial_number', 'item_type' (override with `.groups` argument)ggplot(summarized_log,
aes(x= trial_number, y=medianRT, colour=item_type)) +
geom_line() +
geom_errorbar(aes(ymin=medianRT-SE,
ymax=medianRT+SE),
width=0.8, size = 0.8, position = position_dodge(width = 0.2),
show.legend = FALSE, alpha = 0.8) +
geom_point(position = position_dodge(width = 0.2), size=2.5) +
geom_line(size=1.2, position = position_dodge(width = 0.2)) +
ylab("log(RT) [msec]") +
theme_gray() + theme(
panel.grid.minor = element_blank(),
plot.title = element_text(hjust=0.5, size=15, face="bold"),
axis.title.x = element_text(size=18, face='bold'),
axis.title.y = element_text(size=18, face='bold'),
axis.text = element_text(size=15),
legend.title = element_blank(),
legend.text = element_text(size=14, face='bold'),
strip.text = element_text(size=8, face='bold'),
legend.key.size = unit(2.5, 'lines')) +
scale_x_continuous(name="Trial Number", breaks =c(2,4,6,8)) +
facet_grid(item_type ~ trial_complexity)
Preference test
raw
overall
pref_data %>%
ggplot(aes(x = stim_type_selected)) +
geom_histogram(stat = "count")## Warning: Ignoring unknown parameters: binwidth, bins, pad
by block type
pref_data %>%
ggplot(aes(x = stim_type_selected)) +
geom_histogram(stat = "count") +
facet_wrap(~block_type)## Warning: Ignoring unknown parameters: binwidth, bins, pad
by block number
pref_data %>%
ggplot(aes(x = stim_type_selected)) +
geom_histogram(stat = "count") +
facet_wrap(~block_number) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))## Warning: Ignoring unknown parameters: binwidth, bins, pad
relationship with RT
overall
pref_data %>%
ggplot(aes(x = stim_type_selected,
y = log(trial_looking_time))) +
geom_point(alpha = 0.05,
position = position_jitter(width = 0.3)) +
stat_summary(fun.data = "mean_cl_boot") 
by block type
pref_data %>%
ggplot(aes(x = stim_type_selected,
y = log(trial_looking_time))) +
geom_point(alpha = 0.05,
position = position_jitter(width = 0.3)) +
stat_summary(fun.data = "mean_cl_boot") +
facet_wrap(~block_type)
RT and block num
how long participants spent on the pref test
by block type
pref_data %>%
group_by(block_number, block_type) %>%
summarise(meanRT=mean(log(trial_looking_time)),
maxRT=max(log(trial_looking_time)),
minRT=min(log(trial_looking_time)),
medianRT=median(log(trial_looking_time)),
Std=sd(log(trial_looking_time)),
SE = std.error(log(trial_looking_time)),
n = n()) %>%
ggplot(
aes(x= block_number, y=medianRT, colour=block_type)) +
geom_line() +
geom_errorbar(aes(ymin=medianRT-SE,
ymax=medianRT+SE),
width=0.8, size = 0.8, position = position_dodge(width = 0.2),
show.legend = FALSE, alpha = 0.8) +
geom_point(position = position_dodge(width = 0.2), size=2.5) +
geom_line(size=1.2, position = position_dodge(width = 0.2)) +
ylab("log(RT) [msec]") +
theme_gray() + theme(
panel.grid.minor = element_blank(),
plot.title = element_text(hjust=0.5, size=15, face="bold"),
axis.title.x = element_text(size=18, face='bold'),
axis.title.y = element_text(size=18, face='bold'),
axis.text = element_text(size=15),
legend.title = element_blank(),
legend.text = element_text(size=14, face='bold'),
strip.text = element_text(size=8, face='bold'),
legend.key.size = unit(2.5, 'lines')) +
scale_x_continuous(name="Block Number") +
facet_wrap(~block_type) ## `summarise()` regrouping output by 'block_number' (override with `.groups` argument)
by choice
pref_data %>%
group_by(block_number, stim_type_selected) %>%
summarise(meanRT=mean(log(trial_looking_time)),
maxRT=max(log(trial_looking_time)),
minRT=min(log(trial_looking_time)),
medianRT=median(log(trial_looking_time)),
Std=sd(log(trial_looking_time)),
SE = std.error(log(trial_looking_time)),
n = n()) %>%
ggplot(
aes(x= block_number, y=medianRT, colour=stim_type_selected)) +
geom_line() +
geom_errorbar(aes(ymin=medianRT-SE,
ymax=medianRT+SE),
width=0.8, size = 0.8, position = position_dodge(width = 0.2),
show.legend = FALSE, alpha = 0.8) +
geom_point(position = position_dodge(width = 0.2), size=2.5) +
geom_line(size=1.2, position = position_dodge(width = 0.2)) +
ylab("log(RT) [msec]") +
theme_gray() + theme(
panel.grid.minor = element_blank(),
plot.title = element_text(hjust=0.5, size=15, face="bold"),
axis.title.x = element_text(size=18, face='bold'),
axis.title.y = element_text(size=18, face='bold'),
axis.text = element_text(size=15),
legend.title = element_blank(),
legend.text = element_text(size=14, face='bold'),
strip.text = element_text(size=8, face='bold'),
legend.key.size = unit(2.5, 'lines')) +
scale_x_continuous(name="Block Number") +
facet_wrap(~stim_type_selected) ## `summarise()` regrouping output by 'block_number' (override with `.groups` argument)
Similarity ratings
Raw
similarity_data %>%
ggplot(aes(x = rating))+
geom_histogram(bins = 30)
by intuitive similarity
similarity_data <- similarity_data %>%
mutate(
complexity = case_when(
grepl("complex", stimulus_left) | grepl("complex", stimulus_right) ~ "complex",
grepl("simple", stimulus_right) | grepl("simple", stimulus_right) ~ "simple"
),
stimulus_left_number = as.numeric(str_extract(stimulus_left, "[[:digit:]]+")),
stimulus_right_number = as.numeric(str_extract(stimulus_right, "[[:digit:]]+")),
similarity = case_when(
stimulus_left_number == stimulus_right_number ~ "similar",
TRUE ~ "dissimilar"
)
)
similarity_data %>%
ggplot(aes(x = similarity, y= rating)) +
geom_point(alpha = 0.1,
position = position_jitter(width = 0.3)) +
stat_summary(fun.data = "mean_cl_boot") +
facet_wrap(~complexity)
Complexity Ratings
Raw
complexity_data %>%
ggplot(aes(x = rating))+
geom_histogram(bins = 30)
by intuitive complexity
complexity_data <- complexity_data %>%
mutate(
complexity = case_when(
grepl("complex", stimulus) ~ "complex",
grepl("simple", stimulus) ~ "simple"
)
)
complexity_data %>%
ggplot(aes(x = complexity, y = rating)) +
geom_point(alpha = 0.1,
position = position_jitter(width = 0.3)) +
stat_summary(fun.data = "mean_cl_boot") 